In this work, we propose a new approach, MeshInversion, that is built upon generative prior possessed by Generative Adversarial Networks (GANs) [1]}. GANs are typically known for their exceptional ability to capture comprehensive knowledge [2]}, [3]}, [4]}, empowering the success of GAN inversion in image restoration [5]}, [6]} and point cloud completion [7]}.
We believe that by training a GAN to synthesize 3D shapes in the form of a topology-aligned texture and deformation map, one could enable the generator to capture rich prior knowledge of a certain object category, including high-level semantics, object geometries, and texture details.
